🤖 Chatbot Logic Flow & Architecture

1. RAG (Retrieval-Augmented Generation) Pipeline

💬
User Input
User sends message: "I have a headache and fever"
🔍
Knowledge Search
System searches knowledge base for "headache" and "fever" keywords
📚
Context Retrieval
Finds relevant medical information about symptoms and conditions
🧠
LLM Processing
Sends context + user query to OpenAI/Claude API
💡
Response Generation
LLM generates response based on your knowledge base
✅
Response Delivery
System adds disclaimers and sends response to user

2. Overall System Architecture Flow

1
User Interface
React/Next.js chat interface where users interact
↓
2
API Gateway
Next.js API routes handle requests, authentication, and routing
↓
3
Chat Engine
Core logic for message processing and conversation management
↓
4
Knowledge Retrieval
Search PostgreSQL database or vector store for relevant information
↓
5
LLM Integration
Send context + query to OpenAI/Claude API
↓
6
Response Processing
Add medical disclaimers, save to chat history, return to user

3. Database Structure & Relationships

knowledge_entries
id: SERIAL PRIMARY KEY
title: VARCHAR(255)
content: TEXT
category: VARCHAR(100)
keywords: TEXT[]
confidence_level: VARCHAR(20)
medical_reviewed: BOOLEAN
created_at: TIMESTAMP
chat_sessions
id: SERIAL PRIMARY KEY
user_id: VARCHAR(100)
started_at: TIMESTAMP
context: JSONB
status: VARCHAR(20)
chat_messages
id: SERIAL PRIMARY KEY
session_id: INTEGER REFERENCES chat_sessions(id)
sender_type: VARCHAR(10) -- 'user' or 'bot'
content: TEXT
timestamp: TIMESTAMP

4. Admin Knowledge Management Workflow

📝
Create Content
Admin adds new medical information via dashboard
👨‍⚕️
Medical Review
Healthcare professional reviews and approves content
🔄
Version Control
System tracks changes and maintains version history
🚀
Deploy Live
Approved content becomes available to chatbot
📊
Monitor Usage
Analytics show how content performs in conversations
🔧
Iterate & Improve
Update content based on user feedback and gaps

5. Context Management Flow

1
New Session
User starts conversation, system creates session_id
2
Store Context
Each message updates conversation context (symptoms, preferences)
3
Context Retrieval
Before responding, system loads recent conversation history
4
Contextual Response
LLM generates response using both knowledge base and conversation context
5
Session Persistence
Context saved for future conversations (user can resume later)

6. Maintenance & Improvement Cycle

Daily
• Review failed queries
• Add missing knowledge
• Monitor chat quality
Weekly
• Analyze chat patterns
• Update knowledge gaps
• Performance optimization
Monthly
• Full knowledge audit
• Medical review cycle
• System improvements

7. Sample Implementation Flow

// Main chat handler function async function handleChatMessage(sessionId, userMessage) { // 1. Load conversation context const context = await getSessionContext(sessionId); // 2. Search knowledge base const relevantKnowledge = await searchKnowledge(userMessage); // 3. Build prompt with context + knowledge const prompt = buildPrompt(context, relevantKnowledge, userMessage); // 4. Get LLM response const response = await callLLM(prompt); // 5. Save message history await saveMessage(sessionId, userMessage, response); // 6. Update context await updateContext(sessionId, userMessage, response); return response; }